Chp 8 - Population dynamics.pdf

BIOL207 General Ecology Population Spatial and Temporal Dynamics Natural population fluctuations Age structure Overshoots and die-offs Cyclic population fluctuations Density dependence and time delays Cyclic behavior of populations...

BIOL207 General Ecology Population Spatial and Temporal Dynamics Natural population fluctuations Age structure Overshoots and die-offs Cyclic population fluctuations Density dependence and time delays Cyclic behavior of populations Delayed density dependence Cycles in laboratory populations Mathematical models for predator-prey cycles Functional and Numerical responses  Population Dynamics Natural fluctuations BIOL207 - General Ecology Natural Fluctuations 3 Introduction All populations experience changes over time for numerous reasons Food availability Nesting sites Predation Competition Disease Parasites Weather/climate Some populations remain relatively stable over long periods Example: the Red Deer population on the Isle of Rum (Scotland) Period of observation = 30 years Stable population between 200 and 400 individuals BIOL207 - General Ecology Natural Fluctuations 4 Introduction Some populations exhibit much wider fluctuations Example: Phytoplankton of Lake Erie Waterborne algal cells fluctuated widely 0 cells/cm3 in June > 7,000 cells/cm3 in September The variations occurred at different temporal scales Days Weeks Months Red deer population is inherently more stable Body size difference Population response time Sensitivity to environmental change Small populations have special features Small bodies impose a greater sensitivity to changes Fast reproduction = faster response to changes BIOL207 - General Ecology Natural Fluctuations 5 Age structure fluctuations In many cases the fluctuations are studied based on population size Sometimes fluctuations are studied by looking at the age structure If a population exhibits a large number of a certain age group this indicates an unusually high birth or death rates in its past Example: Whitefish – Lake Erie (1945-1951) The age was defined from the scales In 1947 – there was a large number of 3yr old fish This suggests that in 1944 there was a large reproduction The offspring dominated in numbers in subsequent years It is clear in 4yr old in 1948, and 5 yr old fish in 1949… BIOL207 - General Ecology Natural Fluctuations 6 Overshoots and Die-Offs Populations may exhibit a fast growth that is controlled by the ecosystem carrying capacity (K) This is called density dependence The density will automatically control the growth When low there will be growth When high the ecosystem K will decrease growth Populations rarely follow a smooth growth towards K In many cases populations grow beyond their carrying capacity This growth is called “Overshoot” Overshoot can occur when K decreases over time A 1st year with high rainfall = more food for herbivores A 2nd year with drought = less food Result: lower K for the herbivore population An overshoot cannot be supported by the habitat These populations exhibit a decline in density below K This process is called “die-off” BIOL207 - General Ecology Natural Fluctuations 7 Overshoots and Die-Offs Case-study - Moose and Wolves on Isle Royale Over time moose exceeded K Followed by massive die-offs from starvation Population estimates (1959 to 2011) indicated that moose and wolf populations had wide fluctuations BIOL207 - General Ecology Natural Fluctuations 8 Overshoots and Die-Offs Natural experiment – Reindeer in St Paul Island, Alaska (1911) The USA government introduced 25 reindeer They provided source of meat for the locals The island had no reindeer predators The reindeer began to reproduce They fed on many items throughout the year They relied on lichens to survive winter By 1938 – population > 2,000 individuals The lichens became rare Reindeer exceeded their K capacity Shortly after the 1938 peak: Die-off occurred Scarce winter food Unusually cold winter The decline continued to 8 individuals (1950) Since 1951 31 reindeer were added to the declining population The government maintained the population a few hundred only BIOL207 - General Ecology Natural Fluctuations 9 Cyclic population fluctuations Populations can experience large fluctuations over time: overshoots and die-offs Over long periods populations oscillate up and down in regular patterns: population cycles Example: long-term records of “gyrfalcon” Falconry was popular in European nobility during the 17th and 18th centuries The gyrfalcon was favored due to its size and beautiful appearance Danish royalty imported these falcons from Iceland The falcons were trapped and transported The exported record covered the 1731 – 1793 period The trend line reveals 10 year cycles After 1790 falconry became less popular The numbers of falcon catch decreased The exports correlates well with falcon abundance in nature BIOL207 - General Ecology Natural Fluctuations 10 Cyclic population fluctuations In some cases population fluctuations occur among species and across a large area Example: Finland Annual surveys in 11 provinces on the abundance of 3 grouse species Capercaillie Black grouse Hazel grouse Monitoring persisted for 20 years Data plotted (see pic) shows ~6yr cycles The species have synchronized cycles The results were observed in all zones BIOL207 - General Ecology Natural Fluctuations 11 Cyclic population fluctuations Inherent cyclic behavior of populations Populations can naturally oscillate above and below their carrying capacity (K) This occurs when they are subjected to environmental variation over time The population oscillation Follows an inherent periodicity Fluctuates up and down Has a different time to close a cycle between species Populations are stable at “K” Population respond by growing when subjected to: Predation Disease Density independent events If growth is rapid – populations can grow above “K” Overshoots are observed in this case This is related to time delays: time between breeding initiation and offspring development Populations will experience die-offs after that – sometimes causing “undershoot” (large reduction) BIOL207 - General Ecology  Population Dynamics Laboratory experiments BIOL207 - General Ecology Natural Fluctuations 13 Cyclic population fluctuations Cycles in Laboratory Populations Population models help us to understand how populations oscillate in regular cycles Models do not identify specific mechanisms of time delays Ecologists investigated real populations in laboratory experiments to discover these mechanisms Delays Delayed density dependence due to stored energy/nutrients Example 1 “Water Flea” – Daphnia G. It is a zooplankton species (lakes) When population is low + abundant food Individuals store excess energy as lipid droplets When populations overshoot > K – adults can still reproduce The offspring can use also the lipid droplets to grow well Eventually the stored energy will be used up The Daphnia population crashes to low numbers BIOL207 - General Ecology Natural Fluctuations 14 Cyclic population fluctuations Cycles in Laboratory Populations Delays Example 2 “Water Flea” – Bosmina L. Similar to Daphnia but does not store much lipids When the population approaches to “K” the numbers decreases fast No buffer for the reduction of food No large oscillation observed in this case – only small ones near “K” The population seems to grow towards K and remain near that level Delayed density dependence due to time delays for development stages It is related to the shift in life history stages (Ex: larvae to adult) Example: Experiment on the Sheep Blowfly (Lucilia C.) – STEP 1 It feeds on the flesh of domestic sheep When the larvae is fed a fixed amount of food (K is defined) – the adults are fed an unlimited amount BIOL207 - General Ecology Natural Fluctuations 15 Cyclic population fluctuations Cycles in Laboratory Populations Would a change in adult food abundance diminish the cycle? Population Remaining  adult food More eggs  Larval food Less larvae Less adults Population  crashed larvae  BIOL207 - General Ecology Natural Fluctuations 16 Cyclic population fluctuations Cycles in Laboratory Populations Example: Experiment on the Sheep Blowfly (Lucilia C.) – STEP 2 Modification to the 1st step: the adults are fed an limited amount of food Low irregular cycles in population fluctuations BIOL207 - General Ecology  Population Dynamics Consumer – Resource cycles BIOL207 - General Ecology Natural Fluctuations 18 Consumer-Resource population cycles The consumers and their resource populations fluctuate in regular cycles impacted by time delays Case study – “Lynx” vs “Snowshoe Hare” Trapping record were acquired from Hudson Bay company (fur manufacturing) The record reflected the natural population fluctuations The cycle period ~ 9 to 10 years Lynx cycles lagging 2 years behind the Hare cycles BIOL207 - General Ecology Natural Fluctuations 19 Consumer-Resource population cycles The consumers and their resource populations fluctuate in regular cycles impacted by time delays Other Canadian large herbivores and their predators follow similar patterns with connected delays Medium sized animals fluctuate with a 9 to 10 year cycles period Herbivores: muskrat, ruffed grouse, ptarmigan High Predators: red fox, marten, mink, goshawks predation Small sized animals fluctuate with a 4 year cycles period Herbivores: vole and lemming More Predators: arctic fox, rough-legged hawk, snowy owl Less prey predators To understand the mechanisms underlying the consumer – resource cycles it is useful to examine More Less them in the context of population models prey predators Less predation BIOL207 - General Ecology Natural Fluctuations 20 Consumer-Resource population cycles Creating artificial predator-prey cycles Experiment on species of mites of citrus Typhlodromus : the predator Six-spotted mite : the prey Experimental setup Mites added to trays containing oranges Oranges are habitat + food for the prey Rubber balls added between oranges Trays had different number of oranges Manipulation On trays with few rubber balls Prey grew well Predators were added - prey got consumed – predator population grew rapidly Prey went extinct – predators went extinct afterwards On trays with more rubber balls More time was needed to observe the extinction of the predator with concealed prey… but same fate eventually If predator dispersal is reduced, both species can coexist better over time BIOL207 - General Ecology Natural Fluctuations 21 Consumer-Resource population cycles Creating artificial predator-prey cycles Mobility of the mites The predatory mites disperse by walking The prey mites use a silk line that they spin to float on wind currents Manipulation Slowing down the predators A mazelike pattern of Vaseline barriers was placed among oranges This slows down the dispersal of the predatory mites Enhancing prey escape Vertical wooden pegs were installed for the prey mites They can be used as jumping-off points Results 3 population cycles over a period of 3 months  BIOL207 - General Ecology  Population Dynamics Mathematical models for predator-prey cycles BIOL207 - General Ecology Predator-Prey models 23 Lotka-Voltera model It incorporates oscillations in predator and prey populations The predator numbers always lagging behind their prey’s Model details The number of predators is designated by “P” The number of prey is designated by “N” Model function: Prey population change rate model dN/dt = rN – cNP rN: exponential growth of a prey population – “r” is the intrinsic growth rate (non density dependent) cNP: loss of prey individuals due to predation “NP” is the probability of a random encounter between predators and prey “c” is the probability that the encounter leads to prey’s capture (predator efficiency) Predator population change rate model dP/dt = acNP – mP acNP: birth rate of the predator population “cNP”: prey consumed by the predator population “a”: conversion of prey (food) into predator offspring mP: death rate of the predator – “m” is the per capita mortality rate BIOL207 - General Ecology Predator-Prey models 24 Lotka-Voltera model Cycling in the Lotka-Voltera model Equilibrium conditions Are defined by a zero change in the populations dN/dt = 0 dP/dt = 0 These are called equilibrium isoclines Prey isocline dN/dt = 0 rN – cNP = 0 rN = cNP P = r/c Predator isocline dP/dt = 0 acNP – mP = 0 acNP = mP N = m/ac BIOL207 - General Ecology Predator-Prey models 25 Lotka-Voltera model Cycling in the Lotka-Voltera model Since isocline graphs have the same variables The graphs can be combined into one X axis – number of predators Y axis – number of prey The center is the joint equilibrium point The resulting graph is: “Joint population trajectory” Cycling It starts when P and N deviates from the center The direction is counter clockwise It predicts the response of one population to the changes in the other BIOL207 - General Ecology Predator-Prey models 26 Lotka-Voltera model Cycling in the Lotka-Voltera model 1 2 3 4 3 2 4 1 BIOL207 - General Ecology  Population Dynamics Functional and Numerical responses BIOL207 - General Ecology Functional and Numerical Responses 28 Functional response It is the relationship between the density of the prey and the rate of consumption by the predator There are 3 potential categories for it Type I Consumption increase with prey density Exponential mode till satiation is reached Predator efficiency is constant Type II The number of prey consumed decreases as density increases Prey search efficiency decline with more prey caught per predator With more prey caught – more difficulties to catch new prey Limited by satiation of the predator Type III Low prey consumption when prey is low Rapid consumption when prey density is moderate Slow consumption when prey density is high (like type II) Limited by satiation of the predator BIOL207 - General Ecology Functional and Numerical Responses 29 Functional response Type III 3 factors can explain the type III responses At low prey densities 1. Prey will have more refuge from predators in their habitat 2. Predators have less practice locating and catching the prey - their efficiency decreases – the catch decreases 3. At low prey densities predators may switch to an alternative more abundant prey At moderate prey densities Predators quickly learn to locate and identify prey This is known as “search image” Experiment on Holling Type III response – the Water Bug Given diet: Isopods + mayfly larvae At moderate to high prey density N. glauca consumed the most abundant one More successful attacks on abundant species At low prey densities Low predation rate Switching to the alternative abundant prey At high prey densities High predation rate Limited by satiation BIOL207 - General Ecology  END BIOL207 - General Ecology